5 citations · 9 across the 6 of their papers we have counts for
6 papers
CD^2: Constrained Dataset Distillation for Few-Shot Class-Incremental Learning
Kexin Bao, Daichi Zhang, Hansong Zhang +3
Few-shot class-incremental learning (FSCIL) receives significant attention from the public to perform classification continuously with a few training samples, which suffers from th…
PKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning
Kexin Baoa, Fanzhao Lin, Zichen Wang +3
Few-shot class-incremental learning (FSCIL) aims to continually adapt a model on a limited number of new-class examples, facing two well-known challenges: catastrophic forgetting a…
Few-shot Class-Incremental Learning via Generative Co-Memory Regularization
Kexin Bao, Yong Li, Dan Zeng +1
Few-shot class-incremental learning (FSCIL) aims to incrementally learn models from a small amount of novel data, which requires strong representation and adaptation ability of mod…
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation
Pengju Wang, Bochao Liu, Weijia Guo +2
Federated learning is a distributed machine learning paradigm designed to protect data privacy. However, data heterogeneity across various clients results in catastrophic forgettin…
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition
Junzheng Zhang, Weijia Guo, Bochao Liu +3
Very low-resolution face recognition is challenging due to the serious loss of informative facial details in resolution degradation. In this paper, we propose a generative-discrimi…
Masked Face Recognition with Generative-to-Discriminative Representations
Shiming Ge, Weijia Guo, Chenyu Li +3
Masked face recognition is important for social good but challenged by diverse occlusions that cause insufficient or inaccurate representations. In this work, we propose a unified…